K-Means Clustering Example | Step-by-Step Solution with Employees Data

In this video, we solve a K-means clustering problem step by step using real employee data with two features: professional experience and technical ability. We cover: How to apply K-means with Euclidean distance Assigning employees to groups after the first iteration Calculating new centroids Evaluating whether the initial cluster choices are good Suggestions for better initialization This walkthrough will help you understand how K-means works in practice and how initialization impacts clustering results. #KMeans #Clustering #MachineLearning #UnsupervisedLearning #DataScience #exampreparation